


'''simple docstring'''







import warnings

from ...utils import is_sklearn_available, requires_backends


if is_sklearn_available():
 from scipy.stats import pearsonr, spearmanr
 from sklearn.metrics import fa_score, matthews_corrcoef


SCREAMING_SNAKE_CASE_   =      (
    """This metric will be removed from the library soon, metrics should be handled with the 🤗 Evaluate """
    """library. You can have a look at this example script for pointers: """
    """https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py"""
)




def 							__lowercase    (   __SCREAMING_SNAKE_CASE					,	__SCREAMING_SNAKE_CASE		)					->       Optional[Any]:
     """simple docstring"""




     warnings.warn(__SCREAMING_SNAKE_CASE					,	__SCREAMING_SNAKE_CASE		)
     requires_backends(__SCREAMING_SNAKE_CASE					,	"""sklearn"""		)
     return (preds == labels).mean()




def 							__lowercase    (   __SCREAMING_SNAKE_CASE					,	__SCREAMING_SNAKE_CASE		)					->       Any:
     """simple docstring"""




     warnings.warn(__SCREAMING_SNAKE_CASE					,	__SCREAMING_SNAKE_CASE		)
     requires_backends(__SCREAMING_SNAKE_CASE					,	"""sklearn"""		)
     __a   			= simple_accuracy(__SCREAMING_SNAKE_CASE					,	__SCREAMING_SNAKE_CASE		)
     __a   			= fa_score(y_true=__SCREAMING_SNAKE_CASE					,	y_pred=__SCREAMING_SNAKE_CASE		)
     return {
         "acc": acc,
         "f1": fa,
         "acc_and_f1": (acc + fa) / 2,
     }




def 							__lowercase    (   __SCREAMING_SNAKE_CASE					,	__SCREAMING_SNAKE_CASE		)					->       Optional[int]:
     """simple docstring"""




     warnings.warn(__SCREAMING_SNAKE_CASE					,	__SCREAMING_SNAKE_CASE		)
     requires_backends(__SCREAMING_SNAKE_CASE					,	"""sklearn"""		)
     __a   			= pearsonr(__SCREAMING_SNAKE_CASE					,	__SCREAMING_SNAKE_CASE		)[0]
     __a   			= spearmanr(__SCREAMING_SNAKE_CASE					,	__SCREAMING_SNAKE_CASE		)[0]
     return {
         "pearson": pearson_corr,
         "spearmanr": spearman_corr,
         "corr": (pearson_corr + spearman_corr) / 2,
     }




def 							__lowercase    (   __SCREAMING_SNAKE_CASE					,	__SCREAMING_SNAKE_CASE					,	__SCREAMING_SNAKE_CASE		)					->       List[Any]:
     """simple docstring"""




     warnings.warn(__SCREAMING_SNAKE_CASE					,	__SCREAMING_SNAKE_CASE		)
     requires_backends(__SCREAMING_SNAKE_CASE					,	"""sklearn"""		)
     assert len(__SCREAMING_SNAKE_CASE		) == len(__SCREAMING_SNAKE_CASE		), F'''Predictions and labels have mismatched lengths {len(__SCREAMING_SNAKE_CASE		)} and {len(__SCREAMING_SNAKE_CASE		)}'''
     if task_name == "cola":
          return {"mcc": matthews_corrcoef(__SCREAMING_SNAKE_CASE					,	__SCREAMING_SNAKE_CASE		)}
     elif task_name == "sst-2":
          return {"acc": simple_accuracy(__SCREAMING_SNAKE_CASE					,	__SCREAMING_SNAKE_CASE		)}
     elif task_name == "mrpc":
          return acc_and_fa(__SCREAMING_SNAKE_CASE					,	__SCREAMING_SNAKE_CASE		)
     elif task_name == "sts-b":
          return pearson_and_spearman(__SCREAMING_SNAKE_CASE					,	__SCREAMING_SNAKE_CASE		)
     elif task_name == "qqp":
          return acc_and_fa(__SCREAMING_SNAKE_CASE					,	__SCREAMING_SNAKE_CASE		)
     elif task_name == "mnli":
          return {"mnli/acc": simple_accuracy(__SCREAMING_SNAKE_CASE					,	__SCREAMING_SNAKE_CASE		)}
     elif task_name == "mnli-mm":
          return {"mnli-mm/acc": simple_accuracy(__SCREAMING_SNAKE_CASE					,	__SCREAMING_SNAKE_CASE		)}
     elif task_name == "qnli":
          return {"acc": simple_accuracy(__SCREAMING_SNAKE_CASE					,	__SCREAMING_SNAKE_CASE		)}
     elif task_name == "rte":
          return {"acc": simple_accuracy(__SCREAMING_SNAKE_CASE					,	__SCREAMING_SNAKE_CASE		)}
     elif task_name == "wnli":
          return {"acc": simple_accuracy(__SCREAMING_SNAKE_CASE					,	__SCREAMING_SNAKE_CASE		)}
     elif task_name == "hans":
          return {"acc": simple_accuracy(__SCREAMING_SNAKE_CASE					,	__SCREAMING_SNAKE_CASE		)}
     else:
          raise KeyError(__SCREAMING_SNAKE_CASE		)




def 							__lowercase    (   __SCREAMING_SNAKE_CASE					,	__SCREAMING_SNAKE_CASE					,	__SCREAMING_SNAKE_CASE		)					->       str:
     """simple docstring"""




     warnings.warn(__SCREAMING_SNAKE_CASE					,	__SCREAMING_SNAKE_CASE		)
     requires_backends(__SCREAMING_SNAKE_CASE					,	"""sklearn"""		)
     if len(__SCREAMING_SNAKE_CASE		) != len(__SCREAMING_SNAKE_CASE		):
          raise ValueError(F'''Predictions and labels have mismatched lengths {len(__SCREAMING_SNAKE_CASE		)} and {len(__SCREAMING_SNAKE_CASE		)}'''		)
     if task_name == "xnli":
          return {"acc": simple_accuracy(__SCREAMING_SNAKE_CASE					,	__SCREAMING_SNAKE_CASE		)}
     else:
          raise KeyError(__SCREAMING_SNAKE_CASE		)








from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


lowerCamelCase_    :			Optional[int]    =	{
    """configuration_graphormer""": ["""GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GraphormerConfig"""],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCamelCase_    :			Union[str, Any]    =	[
							    """GRAPHORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """GraphormerForGraphClassification""",
							    """GraphormerModel""",
							    """GraphormerPreTrainedModel""",
							]


if TYPE_CHECKING:
							from .configuration_graphormer import GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, GraphormerConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_graphormer import (
														    GRAPHORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
														    GraphormerForGraphClassification,
														    GraphormerModel,
														    GraphormerPreTrainedModel,
														)


else:
							import sys

							lowerCamelCase_    :			Optional[Any]    =	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






